How does visual perception change for people with cognitive decline? A Scoping Review
Bibliographic record
Abstract
Abstract Background Visual impairment and its associated functional limitations are a common experience of people living with cognitive decline; however, the underlying mechanisms are not fully understood. Identifying potentially modifiable risk factors for dementia and cognitive impairment is a vital step in developing effective sensory testing and intervention. Objective The current study is a scoping review of the literature investigating the association between visual changes and cognitive decline or dementia, and how this relates to functional difficulties. Design Online databases were searched to highlight relevant research from 2015-August 2022, of which we included 30 items in our final sample. Results The existing literature implicates visual impairment as a risk factor for cognitive decline, with 24 of the 30 studies reporting an association between visual impairment and cognitive decline. Conclusions Most of the studies found an association between visual impairment and cognitive decline, dementia, mild cognitive impairment or cognitive impairment-no dementia. Further research is needed to explore the mechanisms of action underpinning this relationship, including multiple measures of vision across various cognitive domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".